Add Ray env class

src/primaite/game/environment.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional, SupportsFloat, Tuple
+from typing import Any, Dict, Optional, SupportsFloat, Tuple
 
 import gymnasium
 from gymnasium.core import ActType, ObsType
@@ -15,11 +15,11 @@ class PrimaiteGymEnv(gymnasium.Env):
     assumptions about the agent list always having a list of length 1.
     """
 
-    def __init__(self, game: PrimaiteGame, agents: List[ProxyAgent]):
+    def __init__(self, game: PrimaiteGame):
         """Initialise the environment."""
         super().__init__()
         self.game: "PrimaiteGame" = game
-        self.agent: ProxyAgent = agents[0]
+        self.agent: ProxyAgent = self.game.rl_agents[0]
 
     def step(self, action: ActType) -> Tuple[ObsType, SupportsFloat, bool, bool, Dict[str, Any]]:
         """Perform a step in the environment."""
@@ -63,3 +63,21 @@ class PrimaiteGymEnv(gymnasium.Env):
         unflat_space = self.agent.observation_manager.space
         unflat_obs = self.agent.observation_manager.current_observation
         return gymnasium.spaces.flatten(unflat_space, unflat_obs)
+
+
+class PrimaiteRayEnv(gymnasium.Env):
+    """Ray wrapper that accepts a single `env_config` parameter in init function for compatibility with Ray."""
+
+    def __init__(self, env_config: Dict) -> None:
+        """Initialise the environment."""
+        self.env = PrimaiteGymEnv(game=env_config["game"])
+        self.action_space = self.env.action_space
+        self.observation_space = self.env.observation_space
+
+    def reset(self, *, seed: int = None, options: dict = None) -> Tuple[ObsType, Dict]:
+        """Reset the environment."""
+        return self.env.reset(seed=seed)
+
+    def step(self, action: ActType) -> Tuple[ObsType, SupportsFloat, bool, bool, Dict]:
+        """Perform a step in the environment."""
+        return self.env.step(action)

src/primaite/notebooks/training_example_sb3.ipynb

@@ -55,6 +55,53 @@
    "source": [
     "gym = PrimaiteGymEnv(game=game, agents=game.rl_agents)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stable_baselines3 import PPO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = PPO('MlpPolicy', gym)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<stable_baselines3.ppo.ppo.PPO at 0x7f75352526e0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.learn(total_timesteps=1000)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.save(\"deleteme\")"
+   ]
   }
  ],
  "metadata": {